School of Mechanical Electronic & Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China.
Sensors (Basel). 2022 Jul 14;22(14):5256. doi: 10.3390/s22145256.
The optimal scheduling of mine water is a multi-objective, multi-constraint, nonlinear, multi-stage combination of optimization problems, in view of the traditional solution methods with the increase in decision-making variable dimensions facing a large amount of computation, "dimensional disaster" and other problems, the introduction of a new intelligent simulation algorithm-the Whale Optimization Algorithm to solve the optimal scheduling problem of mine water. Aiming at the problem that the Whale Optimization Algorithm itself is prone to local optimization and slow convergence, it has been improved by improving its own parameters and introducing the inertia weight of the particle swarm and has achieved more obvious results. According to the actual situation of Nalinhe No. 2 Mine, the mathematical model of multi-target optimization of mine water is established based on the function of reuse time and reuse cost of mine water as the target function, and the balance of supply and demand of mine water, the water quality requirements of water use points at all levels, the water quantity requirements of reservoirs and the priority of water supply as the constraints. The improved Whale Optimization Algorithm was used to search optimal solution, and the results showed that the adaptability value of the improved Whale Optimization Algorithm was significantly improved compared with before, of which 8.65% and 7.69% were increased in the heating season and non-heating season, and the rate of cost reduction was 46.80% and 36.92%, and the iteration efficiency was also significantly improved, which improved the decision-making efficiency of optimal scheduling and became more suitable for the actual scheduling needs of Nalinhe No. 2 mine.
矿坑水资源优化调度是一个多目标、多约束、非线性、多阶段组合的优化问题，针对传统的求解方法随着决策变量维度的增加而面临大量计算、“维度灾难”等问题，引入了一种新的智能模拟算法——鲸鱼优化算法来解决矿坑水资源优化调度问题。针对鲸鱼优化算法本身容易出现局部优化和收敛缓慢的问题，通过改进其自身参数和引入粒子群的惯性权重对其进行了改进，并取得了更加明显的效果。根据那林河二号矿的实际情况，以复用时间和复用成本函数作为目标函数，建立了矿坑水资源多目标优化的数学模型，并以供需平衡、各级用水点的水质要求、水库水量要求和供水优先级作为约束条件。采用改进的鲸鱼优化算法进行搜索最优解，结果表明，改进后的鲸鱼优化算法的适应度值明显提高，其中供暖季和非供暖季分别提高了 8.65%和 7.69%，成本降低率分别为 46.80%和 36.92%，迭代效率也有明显提高，提高了最优调度的决策效率，更加符合那林河二号矿的实际调度需求。
